
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

__a         =   logging.get_logger(__name__)

__a         =   {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      'imagegpt'
	a     :Union[str, Any]       	=      ['past_key_values']
	a     :Tuple       	=      {
	    'hidden_size': 'n_embd',
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[str]=5_1_2 + 1			,				SCREAMING_SNAKE_CASE_    :     int=3_2 * 3_2			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     int=2_4			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=8			,				SCREAMING_SNAKE_CASE_    :     int=None			,				SCREAMING_SNAKE_CASE_    :     List[Any]="quick_gelu"			,				SCREAMING_SNAKE_CASE_    :     int=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=1e-5			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0.02			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     int=False			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			,				)			->	Optional[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   n_positions
					lowercase_	    =   n_embd
					lowercase_	    =   n_layer
					lowercase_	    =   n_head
					lowercase_	    =   n_inner
					lowercase_	    =   activation_function
					lowercase_	    =   resid_pdrop
					lowercase_	    =   embd_pdrop
					lowercase_	    =   attn_pdrop
					lowercase_	    =   layer_norm_epsilon
					lowercase_	    =   initializer_range
					lowercase_	    =   scale_attn_weights
					lowercase_	    =   use_cache
					lowercase_	    =   scale_attn_by_inverse_layer_idx
					lowercase_	    =   reorder_and_upcast_attn
					lowercase_	    =   tie_word_embeddings

					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	@property
	def 		_lowercase    (			self    :     Optional[int]			)			->	Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					    ]			)




	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     "FeatureExtractionMixin"			,				SCREAMING_SNAKE_CASE_    :     int = 1			,				SCREAMING_SNAKE_CASE_    :     int = -1			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     Optional["TensorType"] = None			,				SCREAMING_SNAKE_CASE_    :     int = 3			,				SCREAMING_SNAKE_CASE_    :     int = 3_2			,				SCREAMING_SNAKE_CASE_    :     int = 3_2			,				)			->	Mapping[str, Any]:
					lowercase_	    =   self._generate_dummy_images(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   dict(preprocessor(images=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			)			)

					return inputs





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




